Presented by Radina P. Soebiyanto1,2

on behalf of Richard Kiang1

1NASA Goddard Space F l ight Center, Code 610.2, Greenbel t , MD2 Goddard Ear th Sc iences Technology & Research (GESTAR) ,

Univers i t ies Space Research Associat ion, Columbia, MD

ESTIMATING THE RISK OF VECTOR-BORNE INFECTIOUS

DISEASE & ACUTE RESPIRATORY INFECTIONS USING SATELLITE

DATA

https://ntrs.nasa.gov/search.jsp?R=20120013505 2017-09-06T20:35:27+00:00Z

Malaria in Thailand, Afghanistan and Korea

Dengue in Indonesia

Avian Influenza in Indonesia

Seasonal Influenza in New York, Arizona and Hong Kong

AGENDA

MALARIA

Cause: Plasmodium spp (protozoan)

Carried by Anopheles mosquito

Plasmodium infecting red

blood cell

Image: Nat’l Geographic

Transmission through female Anopheles bite

Image: Nature Burden: 250 million cases each year

1 million deaths annually

Every 30 seconds a child dies from malaria in Africa

Cost ~ 1.3% of annual economic growth in high prevalence countries

High Risk Group: Pregnant women, children and HIV/AIDS co-infection

Indoor sprayingBed

nets

Artemisin-based Combination TherapyImages: WHO

Treatment and Prevention:

VectorControl

Role of c l imatic and environmental determinants

MALARIA

Malaria Distr ibut ion

Determinants Effect

Temperature Parasite + Vector: development and survival

Rainfall Vector breeding habitat

Land-use, NDVI

Vector breeding habitat

Altitude Vector survival

ENSO Vector development, survival and breeding habitat

MALARIA IN THAILAND

Leading cause of morbidity and mortal ity in Thailand

~50% of population l ive in malariousarea

Most endemic provinces are bordering Myanmar & Cambodia Significant immigrant population

Mae La Camp Largest refugee camp

>30,000 population

MALARIA IN THAILAND

Satellite-observed meteorological & Environmental Parameters for 4 Thailand seasons

MODIS MeasurementsSurface Temperature

AVHRR & MODIS MeasurementsVegetation Index

TRMM MeasurementsRainfall

Neural Network training and validation accuracy

MALARIA IN THAILAND

t = time, T = temperature, P = precipitation, H = humidity, V = NDVIKanchanaburi

Tak

Mae Hong Son

MALARIA IN THAILAND

Actual Malaria IncidenceHindcast Incidence

MALARIA IN AFGHANISTAN

Provinces included in the study

TRMM

MODIS-LST NDVI

Adimi et al. Malaria Journal 2010, 9: 125

MALARIA IN AFGHANISTAN

NDVI and te mpera tu re we re a s t rong ind i ca to r fo r ma la r ia r i sk

Prec ip i ta t ion i s no t a s ign i f i can t fac to r Mala r ia r i sk i s ma in l y due to i r r i g a t i o n a s i m p l i e d f rom the s ign i f i can t con t r ibu t ion f rom NDVI

Ave r a g e R 2 i s 0 . 8 4 5

Shor t ma la r ia t ime se r ies (<2 ye ar s ) po s e a c h a l l e n g e fo r m o d e l i n g a n d p r e d i c t i o n

Identification of potential larval habitat (irrigation and drainage ditches) US Army’s Camp Greaves in South

Korea (N. Kyunggi Province)

43 sample sites with predominant habitats of rice fields (26 sites) and ditches (13 sites)

Classification using pan-sharpened 1-m resolution IKONOS data on a 3.2 x 3.2 km test site

MALARIA IN KOREA

DENGUE

Endemic in more than 110 countr ies Tropical, subtropical, urban, peri-

urban areas Annually infects 50 – 100 mil l ion

people worldwide 12,500 – 25,000 deaths annually Symptoms: fever, headache,

muscle and joint pains, and characterist ic skin rash (similar to measles)

Primari ly transmitted by Aedesmosquitoes Live between 35°N - 35°S latitude,

>1000m elevation Four serotypes exist

Infection from one serotype may give lifelong immunity to that serotype, but only short-term to others

Secondary infection increases the severity risk

Red: Epidemic Dengue, Blue: Aedes Aegypti. Source: CDC

Environmental variables used Temperature, dew point, wind speed, TRMM, NDVI

Modeling method ARIMA – Auto Regressive Integrated Moving Average

Classical time series regression

Accounts for seasonality

DENGUE IN INDONESIA

Result Best-fit model uses TRMM and

Dew Point as inputs

Peak timing can be modeled accurately up to year 2004

Vector control effort by the local government started in the early 2005

The problem First appeared in Hong Kong in 1996-1997, HPAI

has spread to approximately 60 countries. More than 250 million poultry were lost.

35% of the human cases are in Indonesia. Worldwide the mortality rate is 53%, but 81% in Indonesia. In Indonesia, 80% of all fatal cases occurred in 3 adjacent provinces.

Co-infection of human and avian influenza in humans may produce deadly strains of viruses through genetic reassortment.

HPAI H5N1 was found in Delaware in 2004.

The risk of an H5, H7 or H9 pandemic is not reduced or replaced by the 2009 H1N1 pandemic.

AVIAN INFLUENZA

Indonesia has 35% of the world’s human cases with 81% mortal ity. For the rest of the world, mortal i ty is 53%

AVIAN INFLUENZA

80% of deaths in Indones ia occur red in th i s reg ion

H5N1 Transmission Pathways

AVIAN INFLUENZA

HUMANS

POULTRY TRADE

wild birdsdomestic birds

ducks & geese

poultry, products, feed, waste, personnel,

equipment

BIRD TRADE MIGRATORY BIRDS

POULTRYSectors 1&2 Sectors 3&4

LPAI spill over

HPAI spill back

human flu virus

pandemic strain

reassortment

?

NAMRU-2 Bird survei l lance sites on Java

AVIAN INFLUENZA

Buf fer zones can be establ ished to l imit the spread of H5N1 around wetlands and nearby farmlands

ASTER image showing NAMRU-2 bird surveillance

site around Muara Cimanuk estuary

EU’s & UK’s Practice: 3 km protection zone

10 km surveillance zone

Larger restricted zone

Poultry and human outbreaks in Greater Jakarta

AVIAN INFLUENZA

Primary road Secondary road

Distribution centerWet market

River Water body

Distance from outbreaks

Cases vs Meteorological factors

Worldwide annual epidemic

Infects 5 – 20% of population with 500,000 deaths

Economic burden in the US ~US$87.1billion

Spatio-temporal pattern of epidemics vary with latitude

Role of environmental and climatic factors

Temperate regions: distinct annual oscillation with winter peak

Tropics: less distinct seasonality and often peak more than once a year

SEASONAL INFLUENZA

Source: Viboud et al., 2006

Factors implicated in influenza

SEASONAL INFLUENZA

Ex Vivo study showing ef ficient transmission at dry and cold condition [Lowens et a l . , 2007]

Dashed: 20CSolid : 5 C

High temperature (30°C) blocks aerosol transmission but not contact transmission

SEASONAL INFLUENZA

SEASONAL INFLUENZA

Weekly lab-confirmed influenza positive

Daily environmental data were aggregated into weekly

Satell ite-derived data TRMM 3B42

LST - MODIS

Ground station data

DATA

Several techniques were employed, including:

SEASONAL INFLUENZA

ARIMA (AutoRegressiveIntegrated Moving Average) Classical time series regression

Accounts for autocorrelation and seasonality properties

Climatic variables as covariates

Previous week(s) count of influenza is included in the inputs

Results published in PLoS ONE 5(3): 9450, 2010

Neural Network (NN) Artificial intelligence technique

Widely applied for approximating functions,

Classification, and

pattern recognition

Takes into account nonlinear relationship

Radial Basis Function NN with 3 nodes in the hidden layer

Only climatic variables and their lags as inputs/predictors

SEASONAL INFLUENZA

NN models show that ~60% of influenza variabil ity in the US regions can be accounted by meteorological factors

ARIMA model performs better for Hong Kong and Maricopa Previous cases are needed Suggests the role of contact transmission

Temperature seems to be the common determinants for influenza in all regions

HONG KONG MARICOPA COUNTY NEW YORK CITY

NAMRU-2

Wetlands International Indonesia Programme

Cobbs Indonesia

USDA APHIS

WHO SEARO

WRAIR

AFRIMS

Thailand Ministry of Public Health

NDVECC

Mahidol University, Faculty of Tropical Medicine

Safi Najibullah – Formerly at National Malaria and Leishmaniasis Control Programme, Afghan Ministry of Public Health

CDC Influenza Division

ACKNOWLEDGMENT